Milling chatter monitoring under variable cutting conditions based on time series features

2021 ◽  
Vol 113 (9-10) ◽  
pp. 2595-2613
Author(s):  
Kunhong Chen ◽  
Xing Zhang ◽  
Zhao Zhao ◽  
Jia Yin ◽  
Wanhua Zhao
Keyword(s):  
Time Series ◽  
Cutting Conditions ◽  
Milling Chatter

Patterns of Regenerative Milling Chatter Under Joint Influences of Cutting Parameters, Tool Geometries, and Runout

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Jinbo Niu ◽  
Ye Ding ◽  
Zunmin Geng ◽  
LiMin Zhu ◽  
Han Ding
Keyword(s):  
Period Doubling ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Tool Geometries ◽  
Cutting Surface ◽  
Distribution Rule ◽  
Theoretical Predictions ◽  
Machining System ◽  
The Time Domain ◽  
Milling Chatter

The regenerative milling chatter is usually regarded as some kind of bifurcation or chaos behaviors of the machining system. Although several chatter patterns such as the secondary Hopf, the period doubling, and the cyclic fold bifurcations were once reported, their relationships with cutting conditions remain undiscovered. This paper aims to uncover the dynamic mechanism of distinct chatter behaviors in general milling scenarios. First, two complementary methods, i.e., the generalized Runge–Kutta method and the time-domain simulation technique, are presented to jointly study the distribution rule of chatter patterns in stability lobe diagrams for milling processes with general flute-spacing tools considering runout. The theoretical predictions are validated by one published example and two cutting experiments under three different cutting conditions. Furthermore, the cutting signal characteristics and cutting surface topography of distinct chatter patterns are analyzed and compared in detail. On this basis, this paper studies the joint influences of cutting parameters, tool geometries, and runout on regenerative chatter behaviors with the proposed methods.

The Research of Chaos Time Series on Chatter Signal Recognition

2013 ◽  
Vol 584 ◽  
pp. 137-141
Author(s):  
Yong Xiang Jiang ◽  
Bing Du ◽  
Pan Zhang ◽  
San Peng Deng ◽  
Yu Ming Qi
Keyword(s):  
Time Series ◽  
Vibration Signal ◽  
Signal Recognition ◽  
Analysis Method ◽  
Correlation Integral ◽  
Discrete Dynamic System ◽  
Nonlinear Input ◽  
Before And After ◽  
Online Detecting ◽  
Milling Chatter

The online detecting of chatter is the key technology to improve the machining quality. Based on the nonlinear chaos control theory in discrete dynamic system, the processing vibration signal discrete time series is taken as system nonlinear input, the C-C algorithm and correlation integral was used to determine appropriate embedding dim m and time delay τ. Then the phase space is reconstructed by discrete vibration signal. In milling chatters experiment, the time series analysis method is used to get the phase diagram before and after chatter. The chatter phase chart shows the characteristics of chaos and recognized the milling chatter.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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